Metadata-Version: 2.1
Name: ois
Version: 0.2
Summary: Optimal Image Subtraction
Home-page: https://github.com/toros-astro/ois
Author: Martin Beroiz
Author-email: martinberoiz@gmail.com
License: UNKNOWN
Description: # Optimal Image Subtraction (OIS)
        
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        ***OIS*** is a Python package to perform optimal image subtraction on astronomical images.
        It also has a companion command-line program written entirely in C.
        
        OIS offers different methods to subtract images:
        
         * Modulated multi-Gaussian kernel (as described in [Alard&Lupton (1998)][1])
         * Delta basis kernel (as described in [Bramich (2010)][2])
         * Adaptive Delta Basis kernel (as described in [Miller (2008)][3])
        
        Each method can (optionally) simultaneously fit and remove common background.
        
        You can find a Jupyter notebook example with the main features at [http://toros-astro.github.io/ois](http://toros-astro.github.io/ois).
        
        ***
        
        ## Installation
        
        To install the Python module:
        
            $ pip install ois
        
        To instal and run the C command-line program, download this repo to your local machine and execute:
        
            $ git clone https://github.com/toros-astro/ois.git
            $ cd ois
            $ make ois
            $ ./ois --help
        
        The C command-line program is somewhat limited in functionality compared to the Python module.
        Please see the [documentation](http://optimal-image-subtraction.readthedocs.io) for more information.
        
        ***
        
        ## Minimal usage example
        
            >>> from ois import optimal_system
            >>> diff = optimal_system(image, image_ref)[0]
        
        Check the [documentation](http://optimal-image-subtraction.readthedocs.io) for a full tutorial.
        
        ***
        
        ## Other Parameters:
        
        **kernelshape**: shape of the kernel to use. Must be of odd size.
        
        **bkgdegree**: degree of the polynomial to fit the background.
            To turn off background fitting set this to None.
        
        **method**: One of the following strings
        
          * `Bramich`: A Delta basis for the kernel (all pixels fit
              independently). Default method.
        
         * `AdaptiveBramich`: Same as Bramich, but with a polynomial variation across the image. It needs the parameter **poly_degree**, which is the polynomial degree of the variation.
        
          * `Alard-Lupton`: A modulated multi-Gaussian kernel.
              It needs the **gausslist** keyword. **gausslist** is a list of dictionaries containing data of the gaussians used in the decomposition of the kernel. Dictionary keywords are: center, sx, sy, modPolyDeg
        
        Extra parameters are passed to the individual methods.
        
        **poly_degree**: needed only for `AdaptiveBramich`. It is the degree
            of the polynomial for the kernel spatial variation.
        
        **gausslist**: needed only for `Alard-Lupton`. A list of dictionaries with info for the modulated multi-Gaussian. Dictionary keys are:
        
        * **center**: a (row, column) tuple for the center of the Gaussian. Default: kernel center.
        * **modPolyDeg**: the degree of the modulating polynomial. Default: 2
        * **sx**: sigma in x direction. Default: 2.
        * **sy**: sigma in y direction. Deafult: 2.
        
        ***
        
        **Author**: Martin Beroiz
        
        <martinberoiz@gmail.com>
        
        [1]: http://arxiv.org/abs/astro-ph/9712287 "A method for optimal image subtraction"
        [2]: http://arxiv.org/abs/0802.1273 "A New Algorithm For Difference Image Analysis"
        [3]: https://iopscience.iop.org/article/10.1086/588258 "Optimal Image Subtraction Method: Summary Derivations, Applications, and Publicly Shared Application Using IDL"
        
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